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\title[Understand Surrounding with Sparse Feature Points
\hspace{0.8cm}  \insertframenumber \text{/} \inserttotalframenumber]{Understand Surrounding with Sparse Feature Points}

\author{You Li}

\institute{
        \medskip
        Universit\'e de Technologie Belfort-Montb\'eliard, France (UTBM)\\
        Laboratoire Systemes et Transports (SeT)\\
        Equipe ICAP, Groupe PENA\\
        \medskip
        {\emph{email : you.li@utbm.fr}}
}

\date{19 May 2011}


\begin{document}

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\begin{frame}{Outline}
\tableofcontents[currentsections,currentsubsections]
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\section{Introduction}
\subsection{A typical visual perception system}
\begin{frame}{Introduction: a typical visual perception system}
In the author's view, a visual perception system for intelligent vehicle answers these questions:

\begin{figure}
\centering
\includegraphics[width=1\textwidth]{mainfunction.eps}
\caption{Main functions}
\end{figure}
\end{frame}

\subsection{Visual feature and feature points}
\begin{frame}{Introduction: visual feature and feature point}
Behind these functions, are comprehensive exploits of visual features and feature points. 
\begin{figure}
\centering
\includegraphics[width=0.9\textwidth]{featureapp.eps}
\caption{Application of feature}
\end{figure}
\end{frame}

\section{Classic Approaches}
\subsection{Visual odometry}
\begin{frame}{Classic approaches: Visual odometry}
\begin{block}{Visual odometry is a process estimates self-moving parameters.}
\end{block}
\begin{figure}
\centering
\includegraphics[width=0.8\textwidth]{odometry.eps}
\caption{Geometric relationship in visual odometry}
\end{figure}
\end{frame}

\begin{frame}{Classic approaches: Visual odometry}
\begin{block}{Regular Procedure:}\end{block}
\begin{figure}
\centering
\includegraphics[width=1\textwidth]{odometry_procedure.eps}
\caption{Regular procedure of visual odometry}
\end{figure}
\begin{block}{Drawback} RANSAC algorithm in fact rejects minor feature points. However, in complex urban environment, it is not always the case.\end{block} 
\end{frame}

\begin{frame}{Classic approaches: Visual odometry}
\begin{block}{Reference:}\end{block}
{\footnotesize
\begin{thebibliography}{9}
\bibitem[Rod09]{1} S.A. Rodr\'iguez et al., \emph{An Experiment of a 3D Real-Time Robust Visual Odometry for Intelligent Vehicles}, ITSC 2009.
\bibitem[Howard08]{2} A. Howard, \emph{Real-Time Stereo Visual Odometry for Autonomous Ground Vehicles}, IROS 2008.
\bibitem[Kitt09]{3} B.Kitt et al. \emph{Visual Odometry based on Stereo Image Sequences with RANSAC-based Outlier Rejection Scheme}, IV 2010.
\bibitem[geiger]{4} A.Geiger et al. \emph{StereoScan: Dense 3D reconstruction in Real-time}, IV 2011.
\bibitem[Golban]{5} C.Colban et al. \emph{Linear vs. non linear minimization in stereo visual odometry}, IV 2011.
\end{thebibliography}}
\end{frame}

\subsection{Object detection and Tracking}
\begin{frame}{Classic approaches: object detection and tracking}
\begin{block}{Moving object detection and tracking:}\end{block}
\begin{figure}
\centering
\includegraphics[width=1\textwidth]{detection_tracking.eps}
\caption{Regular procedure of visual odometry}
\end{figure}
\end{frame}

\begin{frame}{Classic approaches: object detection and tracking}
\begin{block}{Reference:}\end{block}
{\footnotesize
\begin{thebibliography}{9}
\bibitem[Rabe07]{8} C.Rabe et al., \emph{Fast Detection of Moving Objects in Complex Scenarios}, IV 2007.
\bibitem[kittits10]{6} B.Kitt et al., \emph{Detection and Tracking of Independently Moving Objects in Urban Environments}, ITSC 2010.
\bibitem[P.Lenz]{7} P. Lenz et al., \emph{Sparse Scene Flow Segmentation for Moving Object Detection in Urban Environments}, IV 2011.
\end{thebibliography}}
\end{frame}

\subsection{Object recognition and  classification}
\begin{frame}{Classic approaches: Object recognition and classification}

\begin{block}{Object recognition\textbackslash classification \textbackslash categorization}
Two steps:
\begin{enumerate}
\item Feature detection and description 
\item Learning and training classifier
\end{enumerate}
\end{block}
\begin{columns}[T]
\column{0.5\textwidth}

\begin{block}{Three common Features:}
\begin{enumerate}
\item SIFT or SURF feature descriptor
\item HOG (Histogram of oriented gradients)
\item Haar-like feature
\end{enumerate}
\end{block}

\column{0.5\textwidth}
\begin{block}{Two common learning methods:}
\begin{enumerate}
\item SVM (Support Vector Machine)
\item Adaboost
\end{enumerate}
\end{block}
\end{columns}
\end{frame}

\begin{frame}{Classic approaches: Object recognition and classification}
\begin{block}{SIFT feature: Usually combined with BoW (bag of words) model and SVM learning methods, and always used in general object classification.} \end{block}
\begin{columns}[T]
\column{0.3\textwidth}
\begin{figure}
\centering
\includegraphics[width=1\textwidth]{sift.jpg}
\caption{SIFT feature point}
\end{figure}
\column{0.6\textwidth}
\begin{figure}
\centering
\includegraphics[width=1\textwidth]{siftdescriptor.JPG}
\caption{SIFT feature descriptor}
\end{figure}
\end{columns}
\end{frame}



\begin{frame}{Classic approaches: Object recognition and classification}
\begin{block}{HoG feature: are usually combined with SVM learning method and used in pedestrian or vehicle detection.} \end{block}
\begin{columns}[T]
\column{0.5\textwidth}
\begin{figure}
\centering
\includegraphics[width=1\textwidth]{HoG.jpg}
\caption{HoG feature and results of pedestrian detection}
\end{figure}
\column{0.5\textwidth}
\begin{figure}
\centering
\includegraphics[width=1\textwidth]{HoGdetection.jpg}
\caption{}
\end{figure}
\end{columns}
\end{frame}

\begin{frame}{Classic approaches: Object recognition and classification}
\begin{block}{Haar-like features: are often combined with Adaboost algorithm, and mainly used in face detction} \end{block}
\begin{columns}[T]
\column{0.5\textwidth}
\begin{figure}
\centering
\begin{minipage}[b]{1\textwidth}
\includegraphics[width=0.5\textwidth]{haar1.jpg}\\
\includegraphics[width=1\textwidth]{haarfeatures.jpg}
\caption{Haar-like features}
\end{minipage}
\end{figure}
\column{0.5\textwidth}
\begin{figure}
\centering
\includegraphics[width=1\textwidth]{facedetectresult.jpg}
\caption{Applied in face detection}
\end{figure}
\end{columns}
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\begin{frame}{Classic approaches: Object recognition and classification}
\begin{block}{Reference:}\end{block}
{\footnotesize
\begin{thebibliography}{9}
\bibitem[HoG05]{11} N.Dalal et al, \emph{Histograms of Oriented Gradients for Human Detection}, CVPR 2005.
\bibitem[SIFT99]{12} D.G.Lowe et al., \emph{Object recognition from local scale-invariant features}, ICCV 1999.
\bibitem[Adaboost04]{13} P. Viola et al., \emph{Robust real-time face detection}, International Journal of Computer Vision (IJCV) 2004.
\end{thebibliography}}
\end{frame}

\section{State-of-arts}
\begin{frame}{State-of-arts}
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\end{frame}

\section{Summary and Prospect}
\begin{frame}{Summary and Prospect}
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\end{frame}


\end{document}